*5.2. Results*

In this section, all experiments are repeated five times based on randomly split datasets (refer to Section 5.1.1), and the mean result will be provided.

### 5.2.1. Effect of Knowledge Embedded Sample Model

From Figure 4, we can ge<sup>t</sup> a rough impression that the linear separability of knowledge embedded sample is better than raw SM data. Table 3 presents further the detailed performance of raw SM data samples and knowledge-embedded samples. By comparing XGBoost and SSAE in both sample models, all metrics of both algorithms are improved obviously. It demonstrates that knowledge of electricity measurement is very helpful for NTL recognition. Particularly, knowledge embedded sample model plays a more important role to SSAE because it allows SSAE learning more advanced features from mass samples. On the contrary, SSAE could not learn any knowledge from raw SM data, and the performance of SSAE is similar to XGBoost.


**Table 3.** Comparison about sample embedded knowledge or not.
